CVSep 16, 2025
BiasMap: Leveraging Cross-Attentions to Discover and Mitigate Hidden Social Biases in Text-to-Image GenerationRajatsubhra Chakraborty, Xujun Che, Depeng Xu et al.
Bias discovery is critical for black-box generative models, especiall text-to-image (TTI) models. Existing works predominantly focus on output-level demographic distributions, which do not necessarily guarantee concept representations to be disentangled post-mitigation. We propose BiasMap, a model-agnostic framework for uncovering latent concept-level representational biases in stable diffusion models. BiasMap leverages cross-attention attribution maps to reveal structural entanglements between demographics (e.g., gender, race) and semantics (e.g., professions), going deeper into representational bias during the image generation. Using attribution maps of these concepts, we quantify the spatial demographics-semantics concept entanglement via Intersection over Union (IoU), offering a lens into bias that remains hidden in existing fairness discovery approaches. In addition, we further utilize BiasMap for bias mitigation through energy-guided diffusion sampling that directly modifies latent noise space and minimizes the expected SoftIoU during the denoising process. Our findings show that existing fairness interventions may reduce the output distributional gap but often fail to disentangle concept-level coupling, whereas our mitigation method can mitigate concept entanglement in image generation while complementing distributional bias mitigation.
HCMay 25, 2020
Decentralized is not risk-free: Understanding public perceptions of privacy-utility trade-offs in COVID-19 contact-tracing appsTianshi Li, Jackie, Yang et al.
Contact-tracing apps have potential benefits in helping health authorities to act swiftly to halt the spread of COVID-19. However, their effectiveness is heavily dependent on their installation rate, which may be influenced by people's perceptions of the utility of these apps and any potential privacy risks due to the collection and releasing of sensitive user data (e.g., user identity and location). In this paper, we present a survey study that examined people's willingness to install six different contact-tracing apps after informing them of the risks and benefits of each design option (with a U.S.-only sample on Amazon Mechanical Turk, $N=208$). The six app designs covered two major design dimensions (centralized vs decentralized, basic contact tracing vs. also providing hotspot information), grounded in our analysis of existing contact-tracing app proposals. Contrary to assumptions of some prior work, we found that the majority of people in our sample preferred to install apps that use a centralized server for contact tracing, as they are more willing to allow a centralized authority to access the identity of app users rather than allowing tech-savvy users to infer the identity of diagnosed users. We also found that the majority of our sample preferred to install apps that share diagnosed users' recent locations in public places to show hotspots of infection. Our results suggest that apps using a centralized architecture with strong security protection to do basic contact tracing and providing users with other useful information such as hotspots of infection in public places may achieve a high adoption rate in the U.S.
HCFeb 18, 2019
An Exploration of User and Bystander Attitudes About Mobile Live-Streaming VideoCori Faklaris, Asa Blevins, Matthew O'Haver et al.
Thanks to mobile apps such as Periscope and Facebook Live, live-streaming video is having a moment again. It has not been clear, however, to what extent the current ubiquity of smartphones is impacting this technology's acceptance in everyday social situations and how mobile contexts or affordances will affect and be affected by shifts in social norms and policy debates regarding privacy, surveillance and intellectual property. This ethnographic-style research explores familiarity with and attitudes about mobile live-streaming video and related legal and ethical issues among a sample of "Middle America" participants at two typical outdoor social events: sports tailgating and a rooftop party. In situ observations of n=110 bystanders to the use of a smartphone, including interviews with n=20, revealed that many are not fully aware of when their image or speech is being live-streamed in a casual context and want stronger notifications of and ability to consent to such broadcasting.